Neuigkeiten, Anleitungen und Updates vom Potato-Team.
Potato 2.3.0 führt agentische Annotation mit 12 Trace-Format-Konvertern, Solo Mode für menschlich-LLM-kollaboratives Labeling, Best-Worst Scaling, SSO/OAuth, Parquet-Export und 15 Demo-Projekte ein.
Potato 2.3.0 में agentic annotation के साथ 12 trace format converters, Solo Mode, Best-Worst Scaling, SSO/OAuth, Parquet export, और 15 demo projects प्रस्तुत किए गए हैं।
Potato 2.3.0 introduce l'annotazione agentica con 12 convertitori di formato traccia, Solo Mode per l'etichettatura collaborativa umano-LLM, Best-Worst Scaling, SSO/OAuth, esportazione Parquet e 15 progetti demo.
Potato 2.3.0 introduces agentic annotation with 12 trace format converters, Solo Mode for human-LLM collaborative labeling, Best-Worst Scaling, SSO/OAuth, Parquet export, and 15 demo projects.
Erfahren Sie, wie Sie mit Potatos agentischen Annotationsfunktionen eine vollständige Evaluierungspipeline für KI-Agenten einrichten – von der Trace-Importierung bis zum Annotationsschema-Design.
Potato की agentic annotation सुविधाओं का उपयोग करके AI agent outputs का human evaluation कैसे सेट करें — trace import से annotation schema design तक।
Scopri come configurare la valutazione umana degli output di agenti AI utilizzando le funzionalità di annotazione agentica di Potato, dall'importazione delle tracce alla progettazione degli schemi di annotazione.
Learn how to set up human evaluation of AI agent outputs using Potato's agentic annotation features, from trace import to annotation schema design.
Schritt-für-Schritt-Tutorial zur effizienten Beschriftung großer Datensätze mit Potatos Solo Mode und menschlich-LLM-Kollaboration – Annotationskosten um bis zu 90 % reduzieren.
Human-LLM collaboration के साथ बड़े datasets को कुशलतापूर्वक label करने के लिए Potato के Solo Mode का उपयोग करने पर चरण-दर-चरण tutorial, annotation लागत को 90% तक कम करते हुए।
Tutorial passo per passo sull'utilizzo di Solo Mode di Potato per etichettare in modo efficiente grandi dataset con la collaborazione umano-LLM, riducendo il costo dell'annotazione fino al 90%.
Step-by-step tutorial on using Potato's Solo Mode to efficiently label large datasets with human-LLM collaboration, reducing annotation cost by up to 90%.
Wie man Potatos Web-Agent-Trace-Anzeige nutzt, um autonome Web-Browsing-Agenten mit schrittweisen Screenshots, SVG-Overlays und schrittweisen Annotationsschemata zu evaluieren.
Potato के web agent trace display का उपयोग करके स्वायत्त web browsing agents का मूल्यांकन कैसे करें — चरण-दर-चरण screenshots, SVG overlays, और per-step annotation schemas के साथ।
Come utilizzare il display delle tracce degli agenti web di Potato per valutare agenti di navigazione web autonomi, con screenshot passo per passo, overlay SVG e schemi di annotazione per passo.
How to use Potato's web agent trace display to evaluate autonomous web browsing agents, with step-by-step screenshots, SVG overlays, and per-step annotation schemas.
Potato 2.2.0 adds 9 new annotation schemas, a pluggable export system, MACE competence estimation, 55 validated survey instruments, and remote data sources.
Potato 2.1.0 brings the instance display system, visual AI support for image and video annotation, span linking, multi-field spans, and layout customization.
KI-gestützte Objekterkennung, Pre-Annotation und Klassifizierung für Bild- und Videoaufgaben mit YOLO, Ollama, OpenAI und Claude einrichten.
YOLO, Ollama, OpenAI, और Claude के साथ image और video tasks के लिए AI-powered object detection, pre-annotation, और classification सेट करें।
Configura il rilevamento di oggetti assistito dall'AI, la pre-annotazione e la classificazione per task su immagini e video con YOLO, Ollama, OpenAI e Claude.
Set up AI-powered object detection, pre-annotation, and classification for image and video tasks with YOLO, Ollama, OpenAI, and Claude.
Step-by-step guide for manually converting Label Studio projects, templates, and annotations to Potato format.
Complete guide to integrating Potato with Prolific for crowdsourced annotation, including payment and quality control.
Specialized techniques for annotating contracts, court documents, and regulatory filings with domain expertise.
Best practices for annotating medical images using Potato's standard image annotation features.
How to configure polygon drawing tools for image segmentation, with tips for complex shapes and overlapping regions.
Wie man Unsicherheits-Sampling nutzt, um Annotationen zu priorisieren und den gesamten Beschriftungsaufwand um bis zu 50 % zu reduzieren.
Annotations को prioritize करने और कुल labeling effort को 50% तक कम करने के लिए uncertainty sampling का उपयोग कैसे करें।
Come usare l'uncertainty sampling per prioritizzare le annotazioni e ridurre il lavoro totale di etichettatura fino al 50%.
How to use uncertainty sampling to prioritize annotations and reduce the total labeling effort by up to 50%.
Complete guide to setting up bounding box annotation for computer vision tasks, including label colors and validation.
How to convert your Potato annotations into Hugging Face dataset format for model training and sharing.
Build side-by-side image comparison interfaces for preference ranking, A/B testing, and quality assessment.
We're excited to announce Potato 2.0 with AI-powered features, multimedia support, and active learning capabilities.
Configure AI-powered keyword highlighting to draw annotator attention to important terms and phrases.
Step-by-step instructions for running Potato annotation tasks on MTurk with qualification tests and approval workflows.
Build a complete sentiment classification task with radio buttons, tooltips, and keyboard shortcuts for efficient labeling.
Configure Potato for toxicity detection, hate speech classification, and sensitive content labeling with annotator wellbeing in mind.
Create custom annotation interfaces with HTML templates, CSS styling, and JavaScript interactivity.
Build a language learning annotation task for rating pronunciation quality with audio playback and Likert scales.
Build a speaker identification task with audio waveforms, timestamp markers, and speaker label assignment.
Set up video annotation tasks with precise frame navigation, timestamp markers, and temporal event labeling.
Set up annotation for detecting specific sounds like speech, music, applause, or environmental noises with timestamp spans.
Eine schnelle Anleitung zur Einrichtung Ihres ersten Annotationsprojekts mit Potato – von der Installation bis zu Ihren ersten beschrifteten Daten.
Installation से अपने पहले labeled data तक, Potato के साथ अपना पहला annotation project सेट करने के लिए एक त्वरित गाइड।
Una guida rapida per configurare il tuo primo progetto di annotazione con Potato, dall'installazione ai tuoi primi dati etichettati.
A quick guide to setting up your first annotation project with Potato, from installation to your first labeled data.
How to calculate and interpret Cohen's Kappa, Fleiss' Kappa, and Krippendorff's Alpha for your annotation projects.
Our paper on Potato was accepted at EMNLP 2022. Learn about the research behind the tool and how to cite it in your work.
Best practices for ensuring annotation quality in annotation projects, including practical strategies you can implement with and beyond Potato.
Learn how to set up image classification tasks with thumbnail previews, zoom controls, and multi-label support.
Erfahren Sie, wie Sie OpenAI, Claude oder Gemini nutzen, um Ihren Annotatoren intelligente Hinweise und Vorschläge bereitzustellen.
OpenAI, Claude, या Gemini का उपयोग करके अपने annotators को intelligent hints और suggestions प्रदान करना सीखें।
Scopri come usare OpenAI, Claude o Gemini per fornire suggerimenti e hint intelligenti ai tuoi annotatori.
Learn how to use OpenAI, Claude, or Gemini to provide intelligent hints and suggestions to your annotators.
Create a music annotation task with waveform visualization, 30-second previews, and hierarchical genre labels.
Production deployment guide for Potato including Docker, cloud platforms, and SSL configuration.
An overview of multi-object tracking annotation concepts and how Potato's video annotation capabilities can support basic tracking workflows.
Create an audio emotion classification task with waveform display, playback speed controls, and Likert scales.
Step-by-step tutorial on creating a named entity recognition annotation task with span labels and keyboard shortcuts.
A deep dive into JSON and JSONL data formats, with examples for text, image, audio, and multimodal annotation.
Configure waveform visualization, playback controls, and text correction interfaces for audio transcription tasks.
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